feat: made from_bert work

modeling_lora.py

@@ -174,18 +174,24 @@ class LoRAParametrization(nn.Module):
 
 
 class BertLoRA(BertPreTrainedModel):
-    def __init__(self, config: JinaBertConfig, add_pooling_layer=True, num_adaptions=1):
+    def __init__(self, config: JinaBertConfig, bert: Optional[BertModel] = None, add_pooling_layer=True, num_adaptions=1):
         super().__init__(config)
-        self.bert = BertModel(config, add_pooling_layer=add_pooling_layer)
+        if bert is None:
+            self.bert = BertModel(config, add_pooling_layer=add_pooling_layer)
+        else:
+            self.bert = bert
         self._register_lora(num_adaptions)
         for name, param in super().named_parameters():
             if "lora" not in name:
                 param.requires_grad_(False)
         self.select_task(0)
 
-    def from_bert(self, *args, num_adaptions=1, **kwargs):
-        self.bert = BertModel.from_pretrained(*args, **kwargs)
-        self._register_lora(num_adaptions)
+    @classmethod
+    def from_bert(cls, *args, num_adaptions=1, **kwargs):
+        bert = BertModel.from_pretrained(*args, **kwargs)
+        config = JinaBertConfig.from_pretrained(*args, **kwargs)
+        return cls(config, bert=bert, num_adaptions=num_adaptions)
+
 
     def _register_lora(self, num_adaptions=1, rank=4, lora_dropout_p=0.0, lora_alpha=1):
         self.apply(